基于迁移学习技术的隧道围岩岩性识别方法

Lithology Identification Method of Tunnel Surrounding Rock Based on Transfer Learning Technology

为实现隧道围岩岩性的自动识别与分类,提出了基于迁移学习技术的围岩岩性识别方法。首先,通过采用Inception-ResNet-V2(IRV2)卷积神经网络模型在Image-Net数据集上进行预训练,并利用模型迁移学习技术对岩石图片数据集(包含花岗岩、石灰岩、玄武岩和页岩)进行再训练,获取隧道围岩岩性识别模型;然后,对IRV2进行模型测试,并与ResNet-50、Inception-V3和VGG16三种模型的识别性能进行对比;最后,进行子图像法与整体图像法的识别效果对比试验。实验结果表明:(1)IRV2的各项分类性能指标均表现为最优,且均可达到90%以上,表明该模型可以实现围岩岩性的有效识别与精确分类;(2)对于具有更加突出的纹理、结构和构造等外部特征的岩石图片,模型的识别性能更好;(3)子图像法相比于整体图像法可有效提高模型的识别性能。

In order to realize the automatic recognition and classification of the surrounding rock lithology of the tunnel,a method of lithology recognition based on migration learning technology is proposed.First,pre-training on the Image-Net dataset by using the Inception-ResNet-V2(IRV2)convolutional neural network model,and using model transfer learning technology to retrain the rock image dataset(including granite,limestone,basalt and shale)to obtain The lithology recognition model of the surrounding rock of the tunnel;then,the IRV2 model is tested,and the recognition performance of the three models:ResNet-50,Inception-V3 and VGG16 is compared;finally,the sub-image method and the overall image method are performed Comparative test of recognition effect.The experimental results show that:(1)The various classification performance indicators of IRV2 are all the best,and all can reach more than 90%,indicating that the model can realize the effective identification and accurate classification of surrounding rock lithology;(2)For rock pictures with more prominent texture,structure and structure,the recognition performance of the model is better;(3)The sub-image method can effectively improve the model's performance compared to the overall image method.Identify performance.